Complex multi-step production processes may be prone to failure because of their very complexity. Process failure includes both process deviations (i.e., when one or more process parameters are outside their acceptable ranges) and process shutdowns caused by, for example, incompatible or more extreme process parameter deviations. Often, there can be a substantial cost associated with the failure of complex production processes. Examples of failure-related costs include scrap material, production downtime, equipment repair and servicing, and the like.
Traditionally, process-control methods for complex processes have focused on correcting the failed process by bringing the deviating process parameters back into their acceptable ranges. Such traditional solutions are less than ideal because corrective action is taken only after the process enters a failure condition. Therefore, a failure-related cost has already been incurred for the period during which the process was operating in the failure condition. Additionally, a process shutdown, if one has not already occurred, may be required to correct problems resulting from process-parameter deviations.
What is needed is a means by which an approaching process failure is identified prior to its occurrence, i.e., advance failure prediction. However, advance failure prediction for complex processes is difficult because of the large number of variables that may affect the outcome of a process step, and/or the process as a whole.
For example, the production process for integrated circuits comprises hundreds of process steps, each of which may have dozens of controllable parameters, or inputs, that affect the outcome of the process step, subsequent process steps, and/or the process as a whole. In addition, the impact of the controllable parameters on outcome may vary from process run to process run, day to day, or hour to hour. The typical integrated circuit fabrication process thus has a thousand or more controllable inputs, any number of which may be cross-correlated and have a time-varying, nonlinear relationship with the process outcome. As a result, advance failure prediction of even a single integrated circuit process step is difficult.